ax-java-agent-optimize

# AxAgent Optimize For Java This skill helps an agent write Java code with the generated Ax package `dev.axllm:ax`. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package. ## When To Use - Optimize an AxAgent or reusable program component. - Mine grounded weaknesses from failed agent tasks and keep only playbook proposals that pass the verification gate. - Create evaluator callbacks and persist optimizer artifacts. - Keep optimization runs bounded by explicit budgets and dataset rows. ## Package Facts - Language: Java. - Package: `dev.axllm:ax`. - Package API docs: `API.md` and `axir-api.json`. - Capability manifest: `axir-capabilities.json`. - Runnable examples: `examples/`. - Real network support: yes. - Scripted no-key transport support: yes. - Runtime profiles: `javascript-quickjs`, `python-pyodide`. ## Core Pattern ```java AxGEPA engine = new AxGEPA(reflectionClient, java.util.Map.of()); var result = engine.optimize(request, evaluator); ``` ## Relevant API Surface - Agents And RLM: `Ax.agent`, `AxAgent`, `AxAgent.addChildAgent`, `AxAgent.streamingForward` - Optimizers: `Ax.optimize`, `Ax.playbook`, `AxPlaybook`, `AxBootstrapFewShot`, `AxGEPA`, `OptimizerEngine`, `OptimizerEvaluator` ## Guardrails - Start from package examples for exact native syntax before inventing a new call shape. - Use `provider-api` examples only when the user explicitly has provider credentials available. - Use `no-key` examples for deterministic local checks and provider request mapping. - Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages. - Do not copy repo-maintainer skills from `tools/*/skills/` into user packages. - GEPA returns baselineScore and baselineScores at the artifact top level and in metadata, measured before bootstrap or component changes. Compare metadata.bestScore with baselineScore; the original candidate need not remain on the Pareto frontier. - Baseline and seeded validation use the same capped held-out subset and selection scalarization. Both consume maxMetricCalls. Bootstrap generation has separate limits; metadata.bootstrapMetricCalls plus metadata.totalMetricCalls equals metadata.totalCalls. Without new demos, reuse the baseline evaluation. - Bootstrap demos contain only fresh training inputs and outputs; never recover demos from a retained validation or inactive-child trace tree.